updated docs

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
feast-dev · franciscojavierarceo · Dec 23, 2025 · Dec 19, 2025 · Dec 19, 2025 · Dec 19, 2025
commit 0f94a908c9ba4e9c9ba55f380cb922864be683bc
@@ -268,6 +268,114 @@ To start the feature server in TLS mode, you need to provide the private and pub
 feast serve --key /path/to/key.pem --cert /path/to/cert.pem
 ```
 
+# Static Artifacts Loading
+
+## Overview
+
+Static artifacts loading allows you to load models, lookup tables, and other static resources once during feature server startup instead of loading them on each request. These artifacts are cached in memory and accessible to on-demand feature views.
+
+## Usage
+
+### Create Static Artifacts Module
+
+Create a `static_artifacts.py` file in your feature repository:
+
+```python
+# static_artifacts.py
+from fastapi import FastAPI
+from transformers import pipeline
+
+def load_sentiment_model():
+    """Load sentiment analysis model."""
+    return pipeline(
+        "sentiment-analysis",
+        model="cardiffnlp/twitter-roberta-base-sentiment-latest",
+        device="cpu"
+    )
+
+def load_lookup_tables():
+    """Load static lookup tables."""
+    return {
+        "sentiment_labels": {"LABEL_0": "negative", "LABEL_1": "neutral", "LABEL_2": "positive"},
+        "domain_categories": {"twitter.com": "social", "news.com": "news"},
+    }
+
+def load_artifacts(app: FastAPI):
+    """Load static artifacts into app.state."""
+    app.state.sentiment_model = load_sentiment_model()
+    app.state.lookup_tables = load_lookup_tables()
+
+    # Update global references for access from feature views
+    import example_repo
+    example_repo._sentiment_model = app.state.sentiment_model
+    example_repo._lookup_tables = app.state.lookup_tables
+```
+
+### Use Artifacts in Feature Views
+
+Access pre-loaded artifacts in your on-demand feature views:
+
+```python
+# example_repo.py
+import pandas as pd
+from feast.on_demand_feature_view import on_demand_feature_view
+
+# Global references for static artifacts
+_sentiment_model = None
+_lookup_tables: dict = {}
+
+@on_demand_feature_view(
+    sources=[text_input_request],
+    schema=[
+        Field(name="predicted_sentiment", dtype=String),
+        Field(name="sentiment_confidence", dtype=Float32),
+    ],
+)
+def sentiment_prediction(inputs: pd.DataFrame) -> pd.DataFrame:
+    """Sentiment prediction using pre-loaded artifacts."""
+    global _sentiment_model, _lookup_tables
+
+    results = []
+    for text in inputs["input_text"]:
+        predictions = _sentiment_model(text)
+        label_map = _lookup_tables["sentiment_labels"]
+        best_pred = max(predictions, key=lambda x: x["score"])
+        predicted_sentiment = label_map[best_pred["label"]]
+        confidence = best_pred["score"]
+
+        results.append({
+            "predicted_sentiment": predicted_sentiment,
+            "sentiment_confidence": confidence,
+        })
+
+    return pd.DataFrame(results)
+```
+
+### Start Feature Server
+
+```bash
+feast serve
+```
+
+The server will automatically load static artifacts during startup.
+
+## Supported Artifact Types
+
+- Small to medium ML models
+- Lookup tables and reference data
+- Configuration parameters
+- Pre-computed embeddings
+
+## Example Template
+
+The PyTorch NLP template demonstrates static artifacts loading:
+
+```bash
+feast init my-nlp-project -t pytorch_nlp
+cd my-nlp-project/feature_repo
+feast serve
+```
+
 # Online Feature Server Permissions and Access Control
 
 ## API Endpoints and Permissions